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Tanuki.py Alternatives
Similar projects and alternatives to tanuki.py
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Free-AUTO-GPT-with-NO-API
Discontinued Free Auto GPT with NO paids API is a repository that offers a simple version of Auto GPT, an autonomous AI agent capable of performing tasks independently. Unlike other versions, our implementation does not rely on any paid OpenAI API, making it accessible to anyone. [Moved to: https://github.com/IntelligenzaArtificiale/Free-Auto-GPT]
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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OSGPT
OSGPT is a powerful plugin designed to dynamically load documents from specified folders and create searchable vector databases. Not only does it offer a quick way to query from your documents, but it also allows you to execute CLI commands on the host system, be it Linux/Unix or Windows.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Free-Auto-GPT
Free Auto GPT with NO paids API is a repository that offers a simple version of Auto GPT, an autonomous AI agent capable of performing tasks independently. Unlike other versions, our implementation does not rely on any paid OpenAI API, making it accessible to anyone.
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LLMChat
A Discord chatbot that supports popular LLMs for text generation and ultra-realistic voices for voice chat.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better tanuki.py alternative or higher similarity.
tanuki.py reviews and mentions
Posts with mentions or reviews of tanuki.py.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-11-15.
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[D] Alignment using tests? Will it work?
I came across this open source library a few times across Reddit + HN and something that piqued my interest was their concept around "test-driven alignment".
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I built a tool to make it easy to build cheap and fast LLM applications.
Hey guys, I've spent the last few weeks building Tanuki: https://github.com/Tanuki/tanuki.py
- Tanuki: Alignment-as-Code for LLM Applications
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I built a library for cheap, fast and predictable LLM functions in Python
Hi r/python, Jack here! I'm one of the creators of MonkeyPatch, an easy way to build LLM-powered functions and apps that get cheaper and faster the more you use them.
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Show HN: MonkeyPatch – Cheap, fast and predictable LLM functions in Python
Check out the ‘function_modeler’. Currently it’s OpenAI only, but local models are on the immediate roadmap.
https://github.com/monkeypatch/monkeypatch.py/blob/master/sr...
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MonkeyPatch – Cheap and fast LLM functions in Python
Hi HN, Jack here! I’m one of the creators of MonkeyPatch, an easy way to build LLM-powered functions and apps that get cheaper and faster the more you use them.
For example, if you need to classify PDFs, extract product feedback from tweets, or auto-generate synthetic data, you can spin up an LLM-powered Python function in < 5 minutes to power your application. Unlike existing LLM clients, these functions generate well-typed outputs with guardrails to mitigate unexpected behavior.
After about 200-300 calls, these functions will begin to get cheaper and faster. We’ve seen 8-10x reduction in cost and latency in some use-cases! This happens via progressive knowledge distillation - MonkeyPatch incrementally fine-tunes smaller, cheaper models in the background, tests them against the constraints defined by the developer, and retains the smallest model that meets accuracy requirements, which typically has significantly lower costs and latency.
As an LLM researcher, I kept getting asked by startups and friends to build specific LLM features that they could embed into their applications. I realized that most developers have to either 1) use existing low-level LLM clients (GPT4/Claude), which can be unreliable, untyped, and pricey, or 2) pore through LangChain documentation for days to build something.
We built MonkeyPatch to make it easy for developers to inject LLM-powered functions into their code and create tests to ensure they behave as intended. Our goal is to help developers easily build apps and functions without worrying about reliability, cost, and latency, while following best software engineering practices.
We’re only available in Python currently but actively working on a Typescript version. The repo has all the instructions you need to get up and running in a few minutes.
The world of LLMs is changing by the day and so we’re not 100% sure how MonkeyPatch will evolve. For now, I’m just excited to share what we’ve been working on with the HN community. Would love to know what you guys think!
Open-source repo: https://github.com/monkeypatch/monkeypatch.py
Sample use-cases: https://github.com/monkeypatch/monkeypatch.py/tree/master/ex...
Benchmarks: https://github.com/monkeypatch/monkeypatch.py#scaling-and-fi...
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A note from our sponsor - SaaSHub
www.saashub.com | 29 Apr 2024
Stats
Basic tanuki.py repo stats
10
634
9.2
3 months ago
Tanuki/tanuki.py is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of tanuki.py is Python.
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